{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cex_gts5z8A3"
   },
   "source": [
    "# Semantic Search in arXiv Papers\n",
    "\n",
    "This notebook shows how to retrieve data from the arXiv API and implement semantic search and recency weighting with Superlinked. More specifically, the notebook will include the following steps:\n",
    "\n",
    "Preparation\n",
    "\n",
    "- Retrieving, processing and exploring the data\n",
    "\n",
    "Setting up our vector computer\n",
    "\n",
    "-  Creating a schema\n",
    "-  Creating vector embedding spaces\n",
    "-  Indexing & parsing\n",
    "-  Setting up & filling an in-memory data store\n",
    "\n",
    "Searching\n",
    "\n",
    "- Queries\n",
    "- Weighting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WiSIGpXhGqOq"
   },
   "source": [
    "## Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
    "id": "L37bYnS7wpIH"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "%%PIP COMMAND%%\n",
    "%pip install lxml bs4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dpOj5QkXlDcs"
   },
   "source": [
    "### Setting up a basic logger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "hy-6J9jNlGsF"
   },
   "outputs": [],
   "source": [
    "import logging\n",
    "from datetime import datetime, timedelta, timezone\n",
    "from urllib.parse import urlencode\n",
    "import altair as alt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "\n",
    "from bs4 import BeautifulSoup\n",
    "from dateutil import parser\n",
    "\n",
    "from superlinked import framework as sl\n",
    "\n",
    "alt.renderers.enable(\"mimetype\")\n",
    "\n",
    "# Creating and configuring our logger\n",
    "logging.basicConfig(filename=\"std.log\", format=\"%(asctime)s %(message)s\", filemode=\"w\")\n",
    "logger = logging.getLogger()\n",
    "\n",
    "# Set the logger threshold to DEBUG if you encounter errors\n",
    "logger.setLevel(logging.INFO)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z2w5Upt10mee"
   },
   "source": [
    "## Fetching & processing data from the arXiv API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZRfQX_ZqxHkL",
    "outputId": "e2cc1182-6026-42d7-9fa1-c4bfeed6e3d1"
   },
   "outputs": [],
   "source": [
    "def query_arxiv(\n",
    "    query: str = \"%22large%20language%20models%22\",\n",
    "    max_results: int = 1000,\n",
    "    order_by: str = \"lastUpdatedDate\",\n",
    "    order: str = \"descending\",\n",
    ") -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    Basic function for querying the api that lets us specify the most important parameters.\n",
    "\n",
    "    query: URL encoded string to search for in paper titles and abstracts\n",
    "    max_results: maximum amount of results returned by the api\n",
    "    order_by: variable to order the results by\n",
    "    order: descending or ascending based on the order_by parameter\n",
    "    \"\"\"\n",
    "    params = {\n",
    "        \"search_query\": f\"all:{query}\",\n",
    "        \"start\": 0,\n",
    "        \"max_results\": max_results,\n",
    "        \"sortBy\": order_by,\n",
    "        \"sortOrder\": order,\n",
    "    }\n",
    "    url = f\"http://export.arxiv.org/api/query?{urlencode(params)}\"\n",
    "    try:\n",
    "        response = requests.get(url, timeout=5)\n",
    "        response.raise_for_status()\n",
    "        logging.info(f\"Length of response text: {len(response.text)}\")\n",
    "        soup = BeautifulSoup(response.text, \"xml\")\n",
    "        data = []\n",
    "\n",
    "        for entry in soup.find_all(\"entry\"):\n",
    "            data_entry = {tag.name: tag.text.strip() for tag in entry.find_all()}\n",
    "            if \"id\" in data_entry:  # Ensure there is an 'id' field\n",
    "                data.append(data_entry)\n",
    "\n",
    "        logging.info(f\"{len(data)} entries found\")\n",
    "        return pd.DataFrame(data)\n",
    "\n",
    "    except requests.exceptions.RequestException as e:\n",
    "        logging.error(f\"Error during request: {e}\")\n",
    "    except Exception as e:\n",
    "        logging.error(f\"Unexpected error: {e}\")\n",
    "\n",
    "    return pd.DataFrame()  # Return an empty DataFrame if there was an error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Wb-mWmLMyInP",
    "outputId": "f4eb7ff9-5945-4448-a6cf-bd95536afd29"
   },
   "outputs": [],
   "source": [
    "# We are using URL encodings here: %22 means \"\" and %20 stands for a space\n",
    "df = query_arxiv(query=\"%22retrieval%20augmented%20generation%22\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IoypUei1LFB_",
    "outputId": "b505e877-1c7f-4cbe-cf6a-9388485bc5b3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "372"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Notice that we set the maximum to 1000 but the api returned less results, meaning\n",
    "# that the number of paper titles and abstracts including our search query is below 1000\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fcpFnnVMB6-t"
   },
   "source": [
    "## Exploring & preparing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2mVcTkuGMqne",
    "outputId": "1eb89539-5dba-4dc9-f1ff-d72a3afce358"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sl.Index(['id', 'updated', 'published', 'title', 'summary', 'author', 'name',\n",
       "       'link', 'primary_category', 'category', 'comment', 'journal_ref', 'doi',\n",
       "       'affiliation'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking all columns\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "pgdIS8YFQyfC"
   },
   "outputs": [],
   "source": [
    "# Feel free to play around more with the data if you want,\n",
    "# but for this application, we will only need a few columns\n",
    "df = pd.DataFrame(df[[\"id\", \"published\", \"title\", \"summary\"]].copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "N2OzJhECyjoV",
    "outputId": "018b0c93-94b3-4a1b-8f60-5cb6ba53265a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>published</th>\n",
       "      <th>title</th>\n",
       "      <th>summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://arxiv.org/abs/2405.14831v1</td>\n",
       "      <td>2024-05-23T17:47:55Z</td>\n",
       "      <td>HippoRAG: Neurobiologically Inspired Long-Term...</td>\n",
       "      <td>In order to thrive in hostile and ever-changin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://arxiv.org/abs/2405.14702v1</td>\n",
       "      <td>2024-05-23T15:37:06Z</td>\n",
       "      <td>G3: An Effective and Adaptive Framework for Wo...</td>\n",
       "      <td>Worldwide geolocalization aims to locate the p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://arxiv.org/abs/2405.14431v1</td>\n",
       "      <td>2024-05-23T11:00:19Z</td>\n",
       "      <td>RaFe: Ranking Feedback Improves Query Rewritin...</td>\n",
       "      <td>As Large Language Models (LLMs) and Retrieval ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  id             published  \\\n",
       "0  http://arxiv.org/abs/2405.14831v1  2024-05-23T17:47:55Z   \n",
       "1  http://arxiv.org/abs/2405.14702v1  2024-05-23T15:37:06Z   \n",
       "2  http://arxiv.org/abs/2405.14431v1  2024-05-23T11:00:19Z   \n",
       "\n",
       "                                               title  \\\n",
       "0  HippoRAG: Neurobiologically Inspired Long-Term...   \n",
       "1  G3: An Effective and Adaptive Framework for Wo...   \n",
       "2  RaFe: Ranking Feedback Improves Query Rewritin...   \n",
       "\n",
       "                                             summary  \n",
       "0  In order to thrive in hostile and ever-changin...  \n",
       "1  Worldwide geolocalization aims to locate the p...  \n",
       "2  As Large Language Models (LLMs) and Retrieval ...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "33tIGa1JHLjL"
   },
   "outputs": [],
   "source": [
    "# Renaming the columns to have more intuitive names\n",
    "df = df.reset_index().rename(columns={\"id\": \"url\", \"index\": \"id\", \"summary\": \"abstract\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "ZVXctsqmHf51"
   },
   "outputs": [],
   "source": [
    "# The api returns the datetimes as a string, which we first parse\n",
    "# in the datetime format and then convert them to timestamps\n",
    "df[\"published_timestamp\"] = [int(parser.parse(date).replace(tzinfo=timezone.utc).timestamp()) for date in df.published]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t7K1orr-SOM9"
   },
   "source": [
    "## Visualizing the timestamps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 482
    },
    "id": "z3TgpiukKAyz",
    "outputId": "b27379e3-0bb9-49fb-c481-a3839b10e960"
   },
   "outputs": [
    {
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       "config": {
        "view": {
         "continuousHeight": 300,
         "continuousWidth": 300
        }
       },
       "data": {
        "name": "data-031cf80704710245c595cf2ebb2a29b3"
       },
       "datasets": {
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",
      "text/plain": [
       "<VegaLite 5 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/display_frontends.html#troubleshooting\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# some quick transformations and an altair histogram\n",
    "years_to_plot: pd.DataFrame = pd.DataFrame(\n",
    "    {\"year_of_publication\": [int(datetime.fromtimestamp(ts).year) for ts in df[\"published_timestamp\"]]}\n",
    ")\n",
    "alt.Chart(years_to_plot).mark_bar().encode(\n",
    "    x=alt.X(\"year_of_publication:N\", title=\"Year of publication\"),\n",
    "    y=alt.Y(\"count()\", title=\"Count of articles\"),\n",
    ").properties(width=400, height=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6hc37uxd0ydT"
   },
   "source": [
    "## Setting up Superlinked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "p8CKnD5U1Fdn"
   },
   "outputs": [],
   "source": [
    "# Setting up the schema according to our inputs\n",
    "class PapersSchema(sl.Schema):\n",
    "    url: sl.String\n",
    "    title: sl.String\n",
    "    abstract: sl.String\n",
    "    published_timestamp: sl.Timestamp\n",
    "    id: sl.IdField\n",
    "\n",
    "\n",
    "papers = PapersSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "66UBkgzA_NTI",
    "outputId": "326099dd-eda7-465b-8c8f-92827d31b207"
   },
   "outputs": [],
   "source": [
    "YEAR_IN_DAYS = 365\n",
    "\n",
    "# Textual characteristics are embedded using a sentence-transformers model\n",
    "abstract_space = sl.TextSimilaritySpace(text=papers.abstract, model=\"sentence-transformers/all-mpnet-base-v2\")\n",
    "title_space = sl.TextSimilaritySpace(text=papers.title, model=\"sentence-transformers/all-mpnet-base-v2\")\n",
    "# Release date is encoded using Superlinked's recency embedding algorithm\n",
    "recency_space = sl.RecencySpace(\n",
    "    timestamp=papers.published_timestamp,\n",
    "    period_time_list=[\n",
    "        sl.PeriodTime(timedelta(days=0.5 * YEAR_IN_DAYS), weight=1),\n",
    "        sl.PeriodTime(timedelta(days=10 * YEAR_IN_DAYS), weight=1),\n",
    "    ],\n",
    "    negative_filter=0.0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "gC5GvTF5BInp"
   },
   "outputs": [],
   "source": [
    "# We create an index of our spaces\n",
    "papers_index = sl.Index(spaces=[abstract_space, title_space, recency_space])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "KTEfX3umMbOE"
   },
   "outputs": [],
   "source": [
    "dataframe_parser = sl.DataFrameParser(\n",
    "    schema=papers,\n",
    "    mapping={\n",
    "        papers.published_timestamp: \"published_timestamp\",\n",
    "        papers.abstract: \"abstract\",\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "JhR4He6iM66t"
   },
   "outputs": [],
   "source": [
    "# Setting a specific end date to ensure reproducibility of the notebook\n",
    "END_OF_APRIL_24_TS = int(datetime(2024, 4, 30, 23, 59).timestamp())\n",
    "EXECUTOR_DATA = {sl.CONTEXT_COMMON: {sl.CONTEXT_COMMON_NOW: END_OF_APRIL_24_TS}}\n",
    "\n",
    "source: sl.InMemorySource = sl.InMemorySource(papers, parser=dataframe_parser)\n",
    "executor: sl.InMemoryExecutor = sl.InMemoryExecutor(\n",
    "    sources=[source], indices=[papers_index], context_data=EXECUTOR_DATA\n",
    ")\n",
    "app: sl.InMemoryApp = executor.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
      "bdc083ab11764324ab7f3db4a3765124",
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      "5661fc3716434478934868316140f7de",
      "007b995972204638844ad5a850fba445",
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      "0a660dc927a2405f9657455cd03a50bb",
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      "2310566a0db54b1eaae86f14b860f756",
      "5c0858df785c4e32ad3cfd5993c094fb",
      "d3251abd8cee40bbbfb883bbd4e987b2",
      "3c6cc08a94a94eceb1cee42f05f3e607"
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    },
    "id": "sX3ainXkNzYO",
    "outputId": "a5639107-21c8-4f10-a5ed-8c2b2224b5ff"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c0b0e583b03e4e2bbdb7273069bd9006",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/12 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d72fa5eea1c241828c12c7e1a3e3f6c1",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "Batches:   0%|          | 0/12 [00:00<?, ?it/s]"
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     "metadata": {},
     "output_type": "display_data"
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   "source": [
    "# IMPORTANT: if you're running this notebook in Google Colab and\n",
    "# this step is taking very long - you might be running an instance without a GPU\n",
    "source.put([df])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nD0x9T_DOQIo"
   },
   "source": [
    "## Understanding recency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 465
    },
    "id": "rTX88MsCN-x8",
    "outputId": "708328e9-3dcf-49de-ff09-32772384d8c2"
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",
      "text/plain": [
       "<VegaLite 5 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/display_frontends.html#troubleshooting\n"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get an intuitive understanding of how recency is weighted for our data,\n",
    "# we can explore the weights using Superlinked's inbuilt RecencyPlotter\n",
    "recency_plotter = sl.RecencyPlotter(recency_space, context_data=EXECUTOR_DATA)\n",
    "recency_plotter.plot_recency_curve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3V1fXSBUObh3"
   },
   "source": [
    "## Defining queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "XOmp8rG_TsWR"
   },
   "outputs": [],
   "source": [
    "TOP_N = 10\n",
    "\n",
    "# A simple query will serve us right when we simply want to search the dataset with a search term\n",
    "# the term will search in both textual fields\n",
    "# and we will have the option to weight certain inputs' importance\n",
    "simple_query = (\n",
    "    sl.Query(\n",
    "        papers_index,\n",
    "        weights={\n",
    "            abstract_space: sl.Param(\"abstract_weight\"),\n",
    "            title_space: sl.Param(\"title_weight\"),\n",
    "            recency_space: sl.Param(\"recency_weight\"),\n",
    "        },\n",
    "    )\n",
    "    .find(papers)\n",
    "    .similar(abstract_space, sl.Param(\"query_text\"))\n",
    "    .similar(title_space, sl.Param(\"query_text\"))\n",
    "    .select([papers.abstract, papers.title, papers.published_timestamp])\n",
    "    .limit(TOP_N)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y0oJWooGVzEQ"
   },
   "source": [
    "## Executing the queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 427,
     "referenced_widgets": [
      "d072c318391b4461950ac426db8b33c2",
      "5ef62bb0c10247a2b5d6aaef9933acf0",
      "b7e05b2a3684400a8c7f4b515cf37345",
      "d2a9b96b9f1045ceb6c7856d42007043",
      "95540b11692a4be885fb442e2bfaf687",
      "5a6457a530f44eccab8aca08088e1d5d",
      "3cfbfd4b9166488ca456c91de242c595",
      "090cd0276235435f8e973df15afadc62",
      "451fb5f128a64e7f973a470148a038c0",
      "8673af492a5b4ba48a2cc1cf05a6dd11",
      "a19b04067d424c05a82e08ca99f16065",
      "d20f5600a7674a7e94c4924765b75066",
      "a0dcc9fb147a47af9ce022a367136286",
      "67a00930652e4e6dae573bf47dca5284",
      "1a6f67da27c84051a4898de0c78f606c",
      "246a7bd5f2934f9dbabcbc95b988f50f",
      "3fb4de390e7f4d08b88ae34e0c41efe3",
      "b9f47e23ed314646ace31737957ef8da",
      "51b48442835548f7bc6b1b68e9781334",
      "6f13c803f9104127b564d256637c33b5",
      "d301653ae4284759b418fb53a241bfcd",
      "e9346407c3694cecb2a9529706ef9f2c"
     ]
    },
    "id": "93SjmNkHOte-",
    "outputId": "04147709-f28a-4900-b823-bfa7465811ed"
   },
   "outputs": [
    {
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       "version_major": 2,
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      },
      "text/plain": [
       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "40d3a089928f4cb9ac86298f8466efc5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>abstract</th>\n",
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       "      <td>Automated Conversion of Static to Dynamic Sche...</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>The interest in updating Large Language Models...</td>\n",
       "      <td>LLMs Instruct LLMs:An Extraction and Editing M...</td>\n",
       "      <td>2024-03-23</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>In customer service technical support, swiftly...</td>\n",
       "      <td>Retrieval-Augmented Generation with Knowledge ...</td>\n",
       "      <td>2024-04-26</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>In the rapidly evolving field of assistive tec...</td>\n",
       "      <td>Towards Standards-Compliant Assistive Technolo...</td>\n",
       "      <td>2024-04-04</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            abstract  \\\n",
       "0  By integrating Artificial Intelligence (AI) wi...   \n",
       "1  In this paper, we explore the potential applic...   \n",
       "2  Design/methodology/approach This research eval...   \n",
       "3  Standard Full-Data classifiers in NLP demand t...   \n",
       "4  There is a compelling necessity from enterpris...   \n",
       "5  The task of converting natural language querie...   \n",
       "6  As Large Language Models (LLMs) and Retrieval ...   \n",
       "7  The interest in updating Large Language Models...   \n",
       "8  In customer service technical support, swiftly...   \n",
       "9  In the rapidly evolving field of assistive tec...   \n",
       "\n",
       "                                               title release_date   id  \n",
       "0  Generative AI for Low-Carbon Artificial Intell...   2024-04-28   69  \n",
       "1  Automated Conversion of Static to Dynamic Sche...   2024-05-08   44  \n",
       "2  Graph database while computationally efficient...   2024-01-15  287  \n",
       "3  Making LLMs Worth Every Penny: Resource-Limite...   2023-11-10  327  \n",
       "4  Fine Tuning LLM for Enterprise: Practical Guid...   2024-03-23  163  \n",
       "5  DFIN-SQL: Integrating Focused Schema with DIN-...   2024-03-01  203  \n",
       "6  RaFe: Ranking Feedback Improves Query Rewritin...   2024-05-23    2  \n",
       "7  LLMs Instruct LLMs:An Extraction and Editing M...   2024-03-23  164  \n",
       "8  Retrieval-Augmented Generation with Knowledge ...   2024-04-26   50  \n",
       "9  Towards Standards-Compliant Assistive Technolo...   2024-04-04  138  "
      ]
     },
     "execution_count": 21,
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    "    simple_query,\n",
    "    query_text=\"cost reduction\",\n",
    "    abstract_weight=1,\n",
    "    title_weight=1,\n",
    "    recency_weight=0,\n",
    ")\n",
    "\n",
    "df = sl.PandasConverter.to_pandas(regular_result)\n",
    "sl.PandasConverter.format_date_column(df, \"published_timestamp\", \"release_date\")"
   ]
  },
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>abstract</th>\n",
       "      <th>title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>By integrating Artificial Intelligence (AI) wi...</td>\n",
       "      <td>Generative AI for Low-Carbon Artificial Intell...</td>\n",
       "      <td>2024-04-28</td>\n",
       "      <td>69</td>\n",
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       "      <th>1</th>\n",
       "      <td>In this paper, we explore the potential applic...</td>\n",
       "      <td>Automated Conversion of Static to Dynamic Sche...</td>\n",
       "      <td>2024-05-08</td>\n",
       "      <td>44</td>\n",
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       "      <td>As Large Language Models (LLMs) and Retrieval ...</td>\n",
       "      <td>RaFe: Ranking Feedback Improves Query Rewritin...</td>\n",
       "      <td>2024-05-23</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Purpose: The purpose of this study is to inves...</td>\n",
       "      <td>Exploring the Potential of Large Language Mode...</td>\n",
       "      <td>2024-05-15</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>In customer service technical support, swiftly...</td>\n",
       "      <td>Retrieval-Augmented Generation with Knowledge ...</td>\n",
       "      <td>2024-04-26</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Accurate evaluation of financial question answ...</td>\n",
       "      <td>FinTextQA: A Dataset for Long-form Financial Q...</td>\n",
       "      <td>2024-05-16</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>This paper introduces xRAG, an innovative cont...</td>\n",
       "      <td>xRAG: Extreme Context Compression for Retrieva...</td>\n",
       "      <td>2024-05-22</td>\n",
       "      <td>4</td>\n",
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       "      <td>Compressing Long Context for Enhancing RAG wit...</td>\n",
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       "      <td>Question-Based Retrieval using Atomic Units fo...</td>\n",
       "      <td>2024-05-20</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>This paper introduces the RAG-RLRC-LaySum fram...</td>\n",
       "      <td>RAG-RLRC-LaySum at BioLaySumm: Integrating Ret...</td>\n",
       "      <td>2024-05-21</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            abstract  \\\n",
       "0  By integrating Artificial Intelligence (AI) wi...   \n",
       "1  In this paper, we explore the potential applic...   \n",
       "2  As Large Language Models (LLMs) and Retrieval ...   \n",
       "3  Purpose: The purpose of this study is to inves...   \n",
       "4  In customer service technical support, swiftly...   \n",
       "5  Accurate evaluation of financial question answ...   \n",
       "6  This paper introduces xRAG, an innovative cont...   \n",
       "7  Large Language Models (LLMs) have made signifi...   \n",
       "8  Enterprise retrieval augmented generation (RAG...   \n",
       "9  This paper introduces the RAG-RLRC-LaySum fram...   \n",
       "\n",
       "                                               title release_date  id  \n",
       "0  Generative AI for Low-Carbon Artificial Intell...   2024-04-28  69  \n",
       "1  Automated Conversion of Static to Dynamic Sche...   2024-05-08  44  \n",
       "2  RaFe: Ranking Feedback Improves Query Rewritin...   2024-05-23   2  \n",
       "3  Exploring the Potential of Large Language Mode...   2024-05-15  24  \n",
       "4  Retrieval-Augmented Generation with Knowledge ...   2024-04-26  50  \n",
       "5  FinTextQA: A Dataset for Long-form Financial Q...   2024-05-16  22  \n",
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       "7  Compressing Long Context for Enhancing RAG wit...   2024-05-06  53  \n",
       "8  Question-Based Retrieval using Atomic Units fo...   2024-05-20  12  \n",
       "9  RAG-RLRC-LaySum at BioLaySumm: Integrating Ret...   2024-05-21   8  "
      ]
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     "metadata": {},
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    }
   ],
   "source": [
    "recency_weighted_result = app.query(\n",
    "    simple_query,\n",
    "    query_text=\"cost reduction\",\n",
    "    abstract_weight=1,\n",
    "    title_weight=1,\n",
    "    recency_weight=5,\n",
    ")\n",
    "\n",
    "df = sl.PandasConverter.to_pandas(recency_weighted_result)\n",
    "sl.PandasConverter.format_date_column(df, \"published_timestamp\", \"release_date\")"
   ]
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  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "oxkj-u8NW8O8"
   },
   "outputs": [],
   "source": [
    "# A quick helper to visualize the effect of recency weighting\n",
    "def get_time_differences(result: sl.QueryResult, alternative_result: sl.QueryResult) -> list[float]:\n",
    "    # Getting the timestamps of both results\n",
    "    result_ts = [entry.fields[\"published_timestamp\"] for entry in result.entries]\n",
    "    alternative_result_ts = [entry.fields[\"published_timestamp\"] for entry in alternative_result.entries]\n",
    "    # Calculating the absolute time difference in seconds\n",
    "    time_diff = list(\n",
    "        np.absolute(  # type: ignore[attr-defined]\n",
    "            np.array(result_ts) - np.array(alternative_result_ts),\n",
    "        )\n",
    "    )\n",
    "    # Rounded time difference in days\n",
    "    time_diff_days = [round(t_d / 3600 / 24, 1) for t_d in time_diff]\n",
    "    return time_diff_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
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    {
     "data": {
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       "[0.0, 0.0, 129.2, 186.7, 34.4, 76.2, 0.8, 43.8, 23.9, 47.8]"
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   ],
   "source": [
    "get_time_differences(regular_result, recency_weighted_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bkVQ0sm5aRxM"
   },
   "source": [
    "You will see that a lot of the positions haven’t changed, but some have!\n",
    "\n",
    "Obviously, this was a pretty basic example. But I hope I was able to make clear why recency can be an important factor. We could’ve also filtered our timestamp data - metadata filtering is a common practice in Advanced RAG systems after all. However, the recency embeddings we used here are generally more nuanced, similar to how text embeddings are more nuanced than regex.\n",
    "\n",
    "Which one will work better for you will depend on your specific use case. It’s important to remember that there are no silver bullets!"
   ]
  }
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